Many of today's handheld devices make use of wireless “connections” for telephony, digital data transfer, geographical positioning and the like. Despite differences in frequency spectra, modulation methods and spectral power densities, the wireless connectivity standards use synchronized data packets to transmit and receive data. In general, all of these wireless capabilities are defined by industry-approved standards (e.g., IEEE 802.11 and 3GPP LTE), which specify the parameters and limits to which devices having those capabilities must adhere.
At any point along the device-development continuum, it may be necessary to test and verify that a device is operating within its standards' specifications. Many such devices are transceivers; that is, they transmit and receive wireless radio frequency (RF) signals. Specialized systems designed for testing such devices typically contain subsystems designed to receive and analyze device-transmitted signals (e.g., vector signal analyzers (VSAs)) and to send signals (e.g., vector signal generators (VSGs)) that subscribe to the industry-approved standards so as to determine whether a device is receiving and processing the wireless signals in accordance with its standard.
It is common for such test systems to generate spurious (or unintended) signals, also known as “spurs,” along with the intended signals. Such spurious signals are often associated with fundamental frequencies and harmonics of those fundamental frequencies of existing signals in the tester. For example, a local oscillator (LO) that “mixes” with another signal will produce sum and difference frequencies of its fundamental frequency and the other signal's frequency with which it is mixed. An LO may also produce a second harmonic that is twice its fundamental frequency. That, too, can mix with another signal to produce sum and difference signal products, and so on. Usually such spurious signals are at much lower power levels than the intended signals at the frequencies of interest.
However, in tests in which power spectral density, for example, is being analyzed, the spectral mask can be quite broad, and an instrument-generated spurious signal could be at the same order of power magnitude of a device-under-test's (DUT's) generated signal near the boundaries of a spectral mask. If those spurious signals are not distinguished from the DUT's signal, they could contribute to an analysis that shows unfavorable results, such as a power spectral density failure.
In general, when spurious signals generated by a test instrument are identified, they may be eliminated using digital signal processing (DSP) filtering techniques. However, “notching out” or eliminating a known instrument spurious signal can also notch out or eliminate a DUT signal that may happen to occur at the same frequency of the spurious signal of the test instrument. Not identifying signal products of the DUT and grouping them with spurious signals of the test instrument may present difficulties and inaccuracies during subsequent signal analysis. For example, if a spurious signal of a DUT is grouped and eliminated along with a spurious signal of a test instrument, the DUT may appear to pass a test when, in fact, the DUT may actually fail a power spectral density analysis. Thus, a signal capture of the test system and subsequent signal analysis may be inaccurate and yield faulty results including, for example, yielding false positive(s) or negative(s) on a standard's prescribed test results criteria.
Ideally, it is advantageous to utilize a test instrument that generates no spurious signals. As a practical matter, however, this may not be realistic for all testing scenarios. Therefore, a need exists for identifying instrument-generated spurious signals and distinguishing them from signal products of one or more DUTs configured within a test system. A need also exists for ensuring that the analysis of test results, such as power spectral density results, is not affected by instrument spurs and for ensuring increased accuracy of test results.